Data Quality Analyst (Salesforce Data Steward)

We are seeking a Data Quality Analyst (Salesforce Data Steward) to ensure the accuracy, completeness, and integrity of customer and account data within Salesforce. This role supports global Sales, Finance, and Operations teams by maintaining high-quality data standards, enabling reporting, and driving scalable, automated data processes.

Beyond traditional stewardship, this role is expected to leverage AI and automation to proactively validate data, surface and resolve exceptions before they cause problems, and ensure that data issues never block critical downstream revenue processes (Quote-to-Cash). The ideal candidate pairs deep Salesforce data expertise with a builder's mindset — someone who would rather automate a recurring validation than run it by hand every week.

Data Quality & Stewardship

Salesforce data quality — Manage and improve data quality across core objects (Accounts, Contacts, Opportunities), ensuring completeness and accuracy of key firmographic fields such as industry, company size, geography, and DUNS.

Enrichment & standardization — Execute data enrichment activities using third-party providers and manual research; validate and standardize inbound data prior to updates.

Cleansing at scale — Perform deduplication, cleansing, and bulk data updates using tools such as DemandTools and Data Loader.

Reporting & monitoring — Develop data quality reports and dashboards (Power BI / Salesforce) to monitor KPIs, track data health, and identify trends.

Governance & compliance — Enforce data governance standards, maintain documentation and metadata, and ensure compliance with privacy and regulatory requirements.

AI & Automation for Data Validation

Automated validation — Design, build, and maintain automated data-validation checks that continuously monitor Salesforce data against business rules, catching errors and gaps in near real time rather than after the fact.

Unblock downstream processes — Ensure data quality issues do not block or delay critical downstream revenue processes (Quote-to-Cash). Proactively detect, flag, and resolve records that would otherwise fail these processes, and build alerting so problems are caught before they stall the business.

Apply AI / LLMs — Use AI and large language models to accelerate data validation, matching, classification, enrichment, and anomaly detection — for example, standardizing messy inbound data, identifying likely duplicates, or explaining why a record failed a rule.

Build automation — Develop and maintain SQL / Python-based data processing, validation pipelines, and automation; reduce manual data work through repeatable, self-service, and scheduled processes.

Partner with technical teams — Collaborate with technical and RevOps teams on integrations, workflows, and system design so that data quality is enforced upstream, at the point of entry, wherever possible.

Measure & improve — Track the impact of automation on data quality and process throughput, and continuously expand coverage of validated fields, objects, and business rules.

4+ years of strong, hands-on experience with Salesforce (SFDC) data management and data models.

Proficiency in SQL and Python for data analysis and automation.

Hands-on experience building automated data validations or data-quality checks, and comfort translating business rules into automated logic.

Experience applying AI/automation to data work (e.g., AI/LLM-assisted matching, classification, enrichment, or anomaly detection), or a demonstrated aptitude and eagerness to do so.

Hands-on experience with DemandTools or similar data transformation/deduplication tools.

Experience with Power BI or the Power Platform; intermediate to advanced Excel skills.

Proven background in data cleansing, deduplication, and large-scale data management.

Strong analytical skills, attention to detail, and bias toward automating repetitive work.

Preferred Qualifications

Understanding of Quote-to-Cash / order management processes (e.g., quoting and approvals, ordering, provisioning, and billing) and how data quality affects them.

Experience with Dun & Bradstreet (D&B) or other enrichment providers.

Experience with Salesforce APIs / SOQL and building integrations or workflow automation.

Experience with Snowflake or other cloud data platforms.

Familiarity with data governance frameworks and master data management concepts.

Experience with AI/LLM tooling or frameworks applied to data quality or process automation.

Salesforce certifications a plus.